Let's say I compute a confusion matrix, in the sense defined here: http://www.gabormelli.com/RKB/Confusion_Matrix
I could easily compute the number of True Negatives (TN), True Positives (TP), False Negatives (FN) and False Positives (FP) but I feel it would be a bit awkward: in my case, all objects belong to at least one class: so a misclassification (e.g. my classifier put an actual "A" in a "B" bin) is not only a FP, it is a FN in the same time. And what if an actual "A+B" object is classified as "B" only: in my current study, it's definitely better than classified in "C" or not classified at all. I have other examples like this...
As you could guess, for me, those are confusing matrix.